Commit message

Dockerfile

@@ -0,0 +1,16 @@
+FROM python:3.9
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+RUN python3 -m pip install --no-cache-dir --upgrade pip
+RUN python3 -m pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+COPY . .
+
+CMD ["panel", "serve", "/code/LangChain_QA_Panel_App.ipynb", "--address", "0.0.0.0", "--port", "7860", "--allow-websocket-origin", "sophiamyang-panel-pdf-qa.hf.space",  "--allow-websocket-origin", "0.0.0.0:7860"]
+
+RUN mkdir /.cache
+RUN chmod 777 /.cache
+RUN mkdir .chroma
+RUN chmod 777 .chroma

Makefile

@@ -0,0 +1,8 @@
+.PHONY: commit push
+
+commit:
+	git add .
+	git commit -m "Commit message"
+
+push: commit
+	git push

app.ipynb

@@ -0,0 +1,245 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os \n",
+    "from langchain.chains import RetrievalQA\n",
+    "from langchain.llms import OpenAI\n",
+    "from langchain.document_loaders import TextLoader\n",
+    "from langchain.document_loaders import PyPDFLoader\n",
+    "from langchain.indexes import VectorstoreIndexCreator\n",
+    "from langchain.text_splitter import CharacterTextSplitter\n",
+    "from langchain.embeddings import OpenAIEmbeddings\n",
+    "from langchain.vectorstores import Chroma\n",
+    "import panel as pn\n",
+    "import tempfile"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/javascript": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        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scripts.forEach( function (oldScript) {\n      var newScript = document.createElement(\"script\");\n      var attrs = [];\n      var nodemap = oldScript.attributes;\n      for (var j in nodemap) {\n        if (nodemap.hasOwnProperty(j)) {\n          attrs.push(nodemap[j])\n        }\n      }\n      attrs.forEach(function(attr) { newScript.setAttribute(attr.name, attr.value) });\n      newScript.appendChild(document.createTextNode(oldScript.innerHTML));\n      oldScript.parentNode.replaceChild(newScript, oldScript);\n    });\n    if (JS_MIME_TYPE in output.data) {\n      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n    }\n    output_area._hv_plot_id = id;\n    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n      window.PyViz.plot_index[id] = Bokeh.index[id];\n    } else {\n      window.PyViz.plot_index[id] = null;\n    }\n  } else if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n    var bk_div = 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EXEC_MIME_TYPE\n    );\n    this.keyboard_manager.register_events(toinsert);\n    // Render to node\n    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n",
+      "application/vnd.holoviews_load.v0+json": ""
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
+       "  font-family: var(--jp-ui-font-size1);\n",
+       "  font-size: var(--jp-ui-font-size1);\n",
+       "  color: var(--jp-ui-font-color1);\n",
+       "}\n",
+       "</style>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pn.extension('texteditor', template=\"bootstrap\", sizing_mode='stretch_width')\n",
+    "pn.state.template.param.update(\n",
+    "    main_max_width=\"690px\",\n",
+    "    header_background=\"#F08080\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file_input = pn.widgets.FileInput(width=300)\n",
+    "\n",
+    "openaikey = pn.widgets.PasswordInput(\n",
+    "    value=\"\", placeholder=\"Enter your OpenAI API Key here...\", width=300\n",
+    ")\n",
+    "prompt = pn.widgets.TextEditor(\n",
+    "    value=\"\", placeholder=\"Enter your questions here...\", height=160, toolbar=False\n",
+    ")\n",
+    "run_button = pn.widgets.Button(name=\"Run!\")\n",
+    "\n",
+    "select_k = pn.widgets.IntSlider(\n",
+    "    name=\"Number of relevant chunks\", start=1, end=5, step=1, value=2\n",
+    ")\n",
+    "select_chain_type = pn.widgets.RadioButtonGroup(\n",
+    "    name='Chain type', \n",
+    "    options=['stuff', 'map_reduce', \"refine\", \"map_rerank\"]\n",
+    ")\n",
+    "\n",
+    "widgets = pn.Row(\n",
+    "    pn.Column(prompt, run_button, margin=5),\n",
+    "    pn.Card(\n",
+    "        \"Chain type:\",\n",
+    "        pn.Column(select_chain_type, select_k),\n",
+    "        title=\"Advanced settings\", margin=10\n",
+    "    ), width=600\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def qa(file, query, chain_type, k):\n",
+    "    # load document\n",
+    "    loader = PyPDFLoader(file)\n",
+    "    documents = loader.load()\n",
+    "    # split the documents into chunks\n",
+    "    text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n",
+    "    texts = text_splitter.split_documents(documents)\n",
+    "    # select which embeddings we want to use\n",
+    "    embeddings = OpenAIEmbeddings()\n",
+    "    # create the vectorestore to use as the index\n",
+    "    db = Chroma.from_documents(texts, embeddings)\n",
+    "    # expose this index in a retriever interface\n",
+    "    retriever = db.as_retriever(search_type=\"similarity\", search_kwargs={\"k\": k})\n",
+    "    # create a chain to answer questions \n",
+    "    qa = RetrievalQA.from_chain_type(\n",
+    "        llm=OpenAI(), chain_type=chain_type, retriever=retriever, return_source_documents=True)\n",
+    "    result = qa({\"query\": query})\n",
+    "    print(result['result'])\n",
+    "    return result"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "convos = []  # store all panel objects in a list\n",
+    "\n",
+    "def qa_result(_):\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = openaikey.value\n",
+    "    \n",
+    "    # save pdf file to a temp file \n",
+    "    if file_input.value is not None:\n",
+    "        file_input.save(\"/.cache/temp.pdf\")\n",
+    "    \n",
+    "        prompt_text = prompt.value\n",
+    "        if prompt_text:\n",
+    "            result = qa(file=\"/.cache/temp.pdf\", query=prompt_text, chain_type=select_chain_type.value, k=select_k.value)\n",
+    "            convos.extend([\n",
+    "                pn.Row(\n",
+    "                    pn.panel(\"\\U0001F60A\", width=10),\n",
+    "                    prompt_text,\n",
+    "                    width=600\n",
+    "                ),\n",
+    "                pn.Row(\n",
+    "                    pn.panel(\"\\U0001F916\", width=10),\n",
+    "                    pn.Column(\n",
+    "                        result[\"result\"],\n",
+    "                        \"Relevant source text:\",\n",
+    "                        pn.pane.Markdown('\\n--------------------------------------------------------------------\\n'.join(doc.page_content for doc in result[\"source_documents\"]))\n",
+    "                    )\n",
+    "                )\n",
+    "            ])\n",
+    "            #return convos\n",
+    "    return pn.Column(*convos, margin=15, width=575, min_height=400)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "qa_interactive = pn.panel(\n",
+    "    pn.bind(qa_result, run_button),\n",
+    "    loading_indicator=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "output = pn.WidgetBox('*Output will show up here:*', qa_interactive, width=630, scroll=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d572f1a081e741cbaca0638d4d0a860e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "BokehModel(combine_events=True, render_bundle={'docs_json': {'c482f79e-56ca-4f46-b90f-25b4af3de8df': {'defs': …"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# layout\n",
+    "pn.Column(\n",
+    "    pn.pane.Markdown(\"\"\"\n",
+    "    ## \\U0001F60A! Question Answering with your PDF file\n",
+    "    \n",
+    "    1) Upload a PDF. 2) Enter OpenAI API key. This costs $. Set up billing at [OpenAI](https://platform.openai.com/account). 3) Type a question and click \"Run\".\n",
+    "    \n",
+    "    \"\"\"),\n",
+    "    pn.Row(file_input,openaikey),\n",
+    "    output,\n",
+    "    widgets\n",
+    "\n",
+    ").servable()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "env1",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.13"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

requirements.txt

@@ -0,0 +1,8 @@
+langchain
+openai
+chromadb
+pypdf
+tiktoken
+panel
+notebook
+jupyter_bokeh